Method to authenticate a user in an electronic device comprising a fingerprint sensor

ABSTRACT

A method to authenticate a user by means of a fingerprint sensor, which is capable of simultaneously acquiring the fingerprints of a hand, the method comprising: an enrollment process (100), during which the features of first fingerprints (Ei) of the fingers of the hand are acquired (101-103), a variation model (Gij) indicating a plausible mutual position variation for each pair of first fingerprints (Ei, Ej) is determined (104) based on the respective features and stored (105); and a comparison process (200), during which features of successive fingerprints (Ei, Ej) of the fingers of the hand are acquired (201-203), a degree of similarity between each one of the first fingerprints (Ei) and each one of the successive fingerprints (Ck) is determined (204) based on a comparison of the respective features, a degree of plausibility of mutual position of pairs of fingerprints (&lt;Ei,Cki&gt;, &lt;Ej,Ckj&gt;) of a set (P) of pairs of fingerprints, each comprising one of the first fingerprints (Ei) and that of the successive fingerprint (Cki) offering the highest degree of similarity, is determined (207, 208) based on the features of the pairs of fingerprints and on the variation model (Gij) of the first fingerprints (Ei, Ej) of the pairs of fingerprints, the user is recognized (209-211) and the variation models (Gij) are updated (212) based on the degrees of similarity and on the mutual position plausibility degrees.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application is a US national stage application under 35 USC § 371of PCT Application No. PCT/GB2020/051773, filed on Jul. 23, 2020, whichclaims priority from United Kingdom Application Numbers 1910543.6, filedon Jul. 23, 2019, and 1913890.8, filed on Sep. 26, 2019 and EPOApplication No. 19188208.3, filed on Jul. 24, 2019, the entirety of eachare hereby fully incorporated by reference herein.

The invention relates to a method to authenticate a user in anelectronic device comprising a fingerprint sensor, which is capable ofsimultaneously acquiring a plurality of fingerprints of a hand.

In particular, the invention finds advantageous, though non-exclusiveapplication in mobile communication devices and, in particular, inso-called smartphones, in which the fingerprint sensor is built-in in arelatively large-sized touchscreen, to which explicit reference will bemade in the description below without because of this losing ingenerality.

A user is usually authenticated in a smartphone having a touchscreen anda fingerprint sensor built-in in the touchscreen by performing a generalenrollment process, which is typically carried out during the first useof the smartphone and during which first fingerprint data of a hand isacquired by means of the fingerprint sensor, is processed in order toextract features of the fingerprints and said features are stored in amemory of the smartphone, and a comparison process, which is typicallycarried out each time the user wants to unlock the screen of thesmartphone and during which successive sets of fingerprint data of thehand are acquired by means of the fingerprint sensor and are processedto extract the corresponding features, which are compared with thefeatures stored in the memory in order to check whether they belong tothe same user.

The fingerprint data acquired with the sensor typically consists of afingerprint image concerning a hand and detected in the area of thetouchscreen.

In particular, a fingerprint user authentication method is known,wherein, both during the enrollment process and during the comparisonprocess, fingerprint data of different fingers of a hand are acquired,and—therefore—the processing of this data must include isolating thefingerprints of the single fingers in order to extract the minutiae ofeach single fingerprint. Furthermore, the processing of the fingerprintdata comprises the verification of the geometry of the fingers of thehand in order to increase the items of information available and, hence,make the authentication more reliable. However, this method only worksif the fingers are forced to touch the touchscreen in predeterminedpositions and, in particular, when they are stretched out and contiguousto one another. The aforesaid limit evidently decreases the user'scomfort during the authentication, for example it slows down thesmartphone screen unlocking operation, if this operation is bound to therecognition of the user.

The object of the invention is to provide a user authentication methodoperating with multiple fingerprints, which is not affected by theaforementioned drawbacks and, at the same time, can be carried out in astraightforward and low-cost manner.

Aspects of the invention are set out in the independent claims. Optionalfeatures are provided in the dependent claims. According to theinvention, there are provided a method to authenticate a user in anelectronic device, an electronic device and a computer program productas set forth in the appended claims.

In an aspect, there is provided a method to authenticate a user in anelectronic device comprising a fingerprint sensor, which is capable ofsimultaneously acquiring a plurality of fingerprints of a hand. Themethod comprises an enrollment process, during which first fingerprintdata of a hand is acquired by means of the fingerprint sensor andprocessed in order to determine features of first fingerprints ofrespective fingers of the hand and said features are stored in memorymeans of the electronic device. The method comprises a comparisonprocess, during which successive fingerprint data of a hand is acquiredby means of the fingerprint sensor and processed in order to determinefeatures of successive fingerprints of respective fingers of the hand tobe compared with the features of the first fingerprints. The enrollmentprocess comprises: (i) for each one of the possible pairs of firstfingerprints, initializing a respective variation model indicating aplausible variation of the mutual position of the two fingerprints basedon the respective features; and (ii) storing the variation models ofeach pair of the first fingerprints in said memory means. The comparisonprocess comprises: (i) determining a degree of similarity between eachone of the first fingerprints and each one of the successivefingerprints based on a comparison of the respective features; (ii)populating a set of pairs of fingerprints, each pair comprising one ofthe first fingerprints and that of the successive fingerprint whichoffers the highest degree of similarity; (iii) determining a degree ofplausibility of the mutual position of each pair relative to every otherpair of said set of pairs of fingerprints based on the features of thefirst and successive fingerprints of the two pairs of fingerprints andbased on the variation model concerning the first fingerprints of thetwo pairs of fingerprints; (iv) recognizing the user based on thedegrees of similarity between said first fingerprints and saidsuccessive fingerprints and on the degrees of plausibility of the mutualposition of the pairs of said set of pairs of fingerprints; and (v)updating said variation models based on said degrees of similarity andon said degrees of plausibility of the mutual position.

The enrollment process may enable data to be obtained for anauthenticated user. In other words, first fingerprint data obtained inthe enrollment process may be that against which future authenticationis based. Data for multiple users may be obtained and stored, such thatany of a number of users may be deemed to be authenticated users.Successive (e.g. subsequent) fingerprint data of a hand which isobtained may then be compared with the stored data from the enrollmentprocess to determine whether that hand corresponds to a hand of anauthenticated user.

The successive (e.g. subsequent) fingerprint data may be obtained afterthe enrollment process. Operation of the electronic device may becontrolled based on whether a verified user of the device isauthenticated by their fingerprint data. Successive fingerprint data maycomprise fingerprint data from a hand which contacts the sensor afterenrollment process has finished.

A variation model may comprise data which indicates of an expectedposition of the user's fingerprints and/or an expected displacementbetween the user's fingerprints. The variation model may provide anindication of the likelihood of a user's fingerprint being located inany of a plurality of regions on the sensor. The variation model may bebased on measured data for the user's fingerprints when contacting thesensor, and e.g. a variation in the locations with which thatfingerprint contacts the sensor. The variation model may not comprise amodel per se.

For any given pair of first fingerprints, the variation model mayprovide an indication of a plausible variation of the mutual position ofthose two fingerprints. The mutual position of two fingerprints maycomprise a position of one fingerprint relative to the otherfingerprint. In other words, it may indicate a displacement from onefingerprint to the other on the sensor. The mutual position of twofingerprints may comprise an indication of the absolute position of thetwo fingerprints on the sensor. It may indicate where the twofingerprints are on the sensor, and the displacement between them.Displacement between the two fingerprints may comprise a rotation of oneof the fingerprints (e.g. a rotation relative to the other fingerprint).

For each pair of fingerprints, the indication of a plausible variationin the mutual position of the two fingerprints may be based on theposition of the two fingerprints in the enrollment process. It may alsobe based on subsequent fingerprint data obtained for that pair offingerprints (e.g. when a final global score is greater than a thirdthreshold value).

A plausible variation for any given position on the sensor may comprisea region of the sensor surrounding that position. Each finger may beexpected to interact with the sensor in a certain place, e.g. eachfingerprint may be expected to contact the sensor at a certain locationon the sensor. For each fingerprint, there may be an associated expectedcontact location. This expected contact location is the location on thesensor that the user's fingerprint is most likely to touch. In practice,each finger may not always touch the exact same spot on the sensor.Subsequent touches of the sensor by the same finger may be clusteredaround the expected contact location. A variation of contact locationsfor the fingerprint may occur around the expected contact location.

A subsequent contact location (on the sensor from a fingerprint) withina selected distance from the expected contact location for a givenfingerprint (but not exactly at the expected contact location for thatfingerprint) may still be determined to be a touch in an expectedcontact region for that fingerprint. The degree of plausibility that thecontact location corresponds to the expected contact location maydecrease with increased distance away from the expected contact locationon the sensor. A region on the contact sensor surrounding the expectedcontact location may be defined within which it is determined plausiblethat fingerprint contact corresponds to the fingerprint associated withthat region. This region for each fingerprint may provide an indicationof plausible variation for the position of that fingerprint.

For any pair of fingerprints, a plausible variation of the mutualposition of the two fingerprints may be defined. A displacement of onefingerprint from the other may have an associated variation. In otherwords, for a pair of fingerprints, one fingerprint may have an expectedcontact location on the sensor relative to a contact location of theother fingerprint on the sensor. This may be relative, and not absolute.For example, irrespective of where one fingerprint contacts the sensor,there may be a known relationship from that contact point to an expectedcontact point of the other fingerprint. A relative relationship may bedefined between respective contact points on the sensor from each of thetwo fingerprints. In practice, the exact contact point of onefingerprint relative to the other may vary.

An expected contact location for one of the fingerprints may be definedbased on a sensed contact location for the other fingerprint. A regionmay be defined which surrounds the expected contact location. Thedefined region may indicate a variation of contact locations on thesensor for which it is plausible that they relate to the relevantfingerprint.

A plausible variation of the mutual position of the two fingerprints maycomprise an indication of an expected contact region on the sensorwithin which it is deemed plausible that a first of the two fingerprintswould contact the sensor, wherein the expected contact region isdetermined based on a contact location on the sensor for the second ofthe two fingerprints. The expected contact region is determined based onan expected displacement of the first of the two fingerprints from thesecond of the two fingerprints.

A variation model indicating a plausible variation of the mutualposition of the two fingerprints may comprise data indicating region(s)of the contact sensor within which it is deemed plausible (e.g. there isa likelihood above a threshold amount) that one of the fingerprints willcontact the sensor, wherein this region is determined based on a contactlocation for the other of the fingerprints. The model may provide dataregarding (e.g. it may encompass all) expected displacements of thefirst of the two fingerprints from the second of the two fingerprints.Where two subsequent fingerprints are sensed within an expecteddisplacement range from one another, it may be deemed plausible thatthese subsequent fingerprints correspond to the first fingerprints. Thevariation model may comprise a probability distribution for thedisplacement of the first of the two fingerprints relative to thesecond.

Initialising a variation model may comprise determining the expecteddisplacements, e.g. determining an expected contact region on the sensorfor one of the two fingerprints based on the contact location of thesecond of the two fingerprints. Updating a variation model may compriseupdating the range of expected displacements, e.g. updating the expectedcontact region. The update may be such that a relative displacementbetween two subsequent fingerprints determined to correspond to thefirst fingerprints falls within the updated range of expecteddisplacements.

Determining a degree of similarity between data from a first fingerprintand data from a second fingerprint may comprise calculating a likelihoodthat the first and second fingerprints are from the same finger. Inother words, it may comprise calculating a probability that the featuresof the first fingerprint are the same as the features of the secondfingerprint.

Recognizing the user may comprise controlling authentication of the userbased on a determined plausibility of the displacement of each pair offingerprints. Controlling authentication may comprise grantingauthentication (e.g. determining that the hand contacting the sensorcorresponds to, e.g. belongs to, a hand of an authenticated user) in theevent that it is determined that a probability that the fingerprintsfrom the successive hand belong to the same hand as the firstfingerprints is above a threshold value. In the event that theprobability is below a threshold value, it is determined that the user(the user associated with the successive fingerprints) is notauthorised.

Updating the variation models may comprise updating the stored data forat least one of: (i) a plausible variation in the mutual position of thefingerprints and (ii) features of the fingerprints. The update may takeinto account the successive fingerprint data if it is determined thatthe successive fingerprint data corresponds to a hand of the user forthe first fingerprint data.

Determining features of fingerprints may comprise identifying valleysand ridges in the skin on the fingerprints. Sensed data from thefingerprint sensor may indicate proximity of a region of a user's handto the sensor. This may be take into account the valleys and ridges oftheir skin. A contour mapping for these may enable a user's fingerprintto be identified. Features of fingerprints may provide data from which auser may be identified, e.g. sensed data may be unique to a particularuser. The features of each one of the first fingerprints may compriseits minutiae and a respective reference point determined based on saidminutiae and the features of each one of the successive fingerprintscomprise its minutiae. Said reference point may consist of thebarycentre of the relating minutiae.

Said variation model may be defined by a bivariate Gaussian functionhaving a mean vector calculated as a function of the features of thecorresponding pair of first fingerprints and a covariance matrix. Themean vector may comprise an expected displacement of the first fingertipof the pair from the second fingertip of the pair. The mean vector mayprovide an indication of an expected contact position for one fingertipof the pair relative to a contact position for the other fingertip inthe pair. The plausibility of variation of the mutual position may bedistributed according to a Gaussian distribution. The Gaussian functionmay be centred on the expected contact position of one fingertiprelative to the other. The plausibility may decrease with increaseddistance from this expected contact position. The decrease inplausibility may follow a Gaussian distribution.

Said mean vector may be initialized to an initial displacement vector,which is calculated as a difference vector between the two referencepoints of the pair of first fingerprints. The covariance matrix may beinitialized to a scalar diagonal matrix. It may provide a symmetricprobability distribution about the expected contact point, for example,with one or two degrees of symmetry.

The degree of similarity may be defined by a confidence score and by arototranslation, both determined based on a comparison of featuresbetween a first fingerprint and a successive fingerprint. The confidencescore may comprise a probability that the features in the firstfingerprint correspond to those in the successive fingerprint, e.g. aprobability that both features belong to the same hand. Therototranslation may comprise at least one of a rotation and atranslation so that the first fingerprint and the successive fingerprintare aligned. For example, the rotation and/or translation required sothat the two fingerprints have the highest degree of similarity. Forexample, the rototranslation may align the first fingerprint with thesuccessive fingerprint.

Each pair of said set of pairs of fingerprints may comprise one of thefirst fingerprints and that of the successive fingerprints which offersthe highest confidence score, and, at the same time, is greater than orequal to a first threshold value. A pair may only be populated if itsrespective first and successive fingerprints have a degree of similarityabove the first threshold value. For example, even if said successivefingerprint has the highest degree of similarity with the firstfingerprint (of all the successive fingerprints), the pair will only bepopulated if that degree of similarity is above the first thresholdvalue. The similarity may be determined based on the confidence score.

Determining a mutual position plausibility degree may comprise computinga displacement vector of each pair relative to every other pair of saidset of pairs of fingerprints, based on the reference points of the firstfingerprints of the two pairs and on the rototranslations between thefirst fingerprint and the successive fingerprint of the two pairs. Itmay comprise computing a probability density of observing thedisplacement vector, based on the variation model concerning the firstfingerprints of the two pairs. Said mutual position plausibility degreemay be defined by said probability density. In other words, the degreeof plausibility may comprise the probability of sensing the mutualposition of the two fingerprints. This probability may be determinedusing the variation model.

Calculating a displacement vector may comprise applying respectiverototranslations to the reference points of the first fingerprints ofthe two pairs so as to obtain two respective new reference points. Itmay comprise computing said displacement vector as rotation of adifference vector between the two new reference points, this rotationbeing equal to an average of the rotation angles of the rototranslationsassociated with the two pairs. Said probability density of thedisplacement vector may be computed taking into account a probabilitydensity with a bivariate Gaussian distribution, which has a mean vectorand a covariance matrix coinciding with the mean vector and with thecovariance matrix, respectively, of said variation model concerning thefirst fingerprints of the two pairs.

Recognizing the user may comprise: (i) computing an initial global scoreby summing the confidence scores associated with the pairs of said setof pairs of fingerprints; (ii) computing a final global score byconsolidating the initial global score by means of the probabilitydensity of the displacement vectors between the pairs of said set ofpairs of fingerprints; and (iii) assuming that said first fingerprintdata and said successive fingerprint data belong to the same user, ifthe final global score is greater than a second threshold value. Saidvariation models may be updated if said final global score is greaterthan a third threshold value. Updating said variation models maycomprise, for each variation model concerning two first fingerprints,updating the respective mean vector and the respective covariance matrixaccording to the displacement vector of those two pairs of said set (P)of pairs of fingerprints comprising the two first fingerprints.

In an aspect, there is provided a method of authenticating a user usingan electronic device comprising a fingerprint sensor. The methodcomprises an enrollment process comprising: obtaining first fingerprintdata of a first hand using the fingerprint sensor; determining, andstoring, features of first fingerprints of respective fingers of thefirst hand; and determining, and storing, for each one of the possiblepairs of first fingerprints, an indication of plausible displacementbetween the two first fingerprints on the fingerprint sensor. The methodcomprises a comparison process comprising: obtaining second fingerprintdata of a subsequent hand using the fingerprint sensor; determiningfeatures of second fingerprints of respective fingers of the subsequenthand; determining a degree of similarity between each one of the firstfingerprints and each one of the second fingerprints based on acomparison of the respective features; identifying pairs offingerprints, each pair comprising one of the first fingerprints and thesecond fingerprint which has the highest degree of similarity to saidone of the first fingerprints; and determining a plausibility of thedisplacement of each pair relative to each of the other pairs based onthe stored indications of plausible displacement between the possiblepairs of first fingerprints. The method comprises controllingauthentication of the user based on the determined plausibility of thedisplacement of each pair of fingerprints.

Determining and storing an indication of plausible displacement betweenthe two fingerprints may comprise initializing and storing a respectivevariation model indicating a plausible variation of the mutual positionof the two fingerprints based on the respective features. This variationmodel may be stored in memory means of the electronic device. Subsequent(successive) fingerprint data may be obtained. Identifying pairs offingerprints may comprise populating a set of pairs of fingerprints.Determining a plausibility of the displacement of each pair relative toeach of the other pairs may comprise determining a degree ofplausibility of the mutual position of each pair relative to every otherpair of said set of pairs of fingerprints based on the features of thefirst and successive fingerprints of the two pairs of fingerprints andbased on the variation model concerning the first fingerprints of thetwo pairs of fingerprints. Controlling authentication of the user basedon the determined plausibility of the displacement of each pair offingerprints may comprise recognizing the user based on the degrees ofsimilarity between said first fingerprints and said successivefingerprints and on the degrees of plausibility of the mutual positionof the pairs of said set of pairs of fingerprints.

In the event that it is determined that a likelihood that the subsequenthand corresponds to the first hand is above a third threshold value,methods may comprise updating at least one of: (i) the stored featuresof the first fingerprints, and (ii) the stored indications of plausibledisplacement between first fingerprints, wherein said update is based onthe second fingerprint data. For example, methods may comprise updatingvariation models based on the degrees of similarity and on the degreesof plausibility of the mutual position.

The degree of similarity between the first and second fingerprints maybe determined based on both: (i) a confidence score of the match ofrespective features of the first and second fingerprints, and (ii) arototranslation between the two fingerprints. Pairs of fingerprints mayonly be identified for pairs where it is determined that the respectivefirst and second fingerprints have a degree of similarity above a firstthreshold value. For example, each set of pairs of fingerprints maycomprise one of the first fingerprints and that of the successivefingerprints which offers the highest confidence score and, at the sametime, is greater than or equal to the first threshold value.

Determining the plausibility of the displacement of each pair relativeto each of the other pairs may be based on: both (i) a reference pointfor the first fingerprint for each of the two pairs and (ii) arototranslation between the first fingerprint and its subsequentfingerprint for each of the two pairs. For example, determining a mutualposition plausibility degree may comprise computing a displacementvector of each pair relative to every other pair of said set of pairs offingerprints, based on the reference points of the first fingerprints ofthe two pairs and on the rototranslations between the first fingerprintand the successive fingerprint of the two pairs. It may comprisecomputing a probability density of observing the displacement vector,based on the variation model concerning the first fingerprints of thetwo pairs. The mutual position plausibility degree may be defined by theprobability density.

Controlling authentication of the user may be based on a final globalscore, which is determined based on both: (i) the determined degree ofsimilarity between the first fingerprints and the second fingerprints,and (ii) the determined plausibility of the displacement of each pair offingerprints. An initial global score may be computed by summing theconfidence scores associated with the pairs of said set of pairs offingerprints. A final global score may be computed by consolidating theinitial global score by means of the probability density of thedisplacement vectors between the pairs of said set of pairs offingerprints. In the event that the final global score is greater than asecond threshold value, it may be determined that the first and secondfingerprint data belong to the same user.

The determined likelihood that the subsequent hand corresponds to thefirst hand may comprise the final global score, and in the event thatthe final global score is above the third threshold value, the methodcomprises updating the at least one of: (i) the stored features of thefirst fingerprints, and (ii) the stored indications of plausibledisplacement between first fingerprints, wherein said update is based onthe second fingerprint data. For example, if the final global score isgreater than a third threshold value, the variation models may beupdated.

In an aspect, there is provided an enrollment method of preparing anelectronic device comprising a fingerprint sensor to authenticate a userusing the fingerprint sensor. The method comprises: obtaining firstfingerprint data of a first hand using the fingerprint sensor;determining, and storing, features of first fingerprints of respectivefingers of the first hand; and determining, for each one of the possiblepairs of first fingerprints, an indication of plausible displacementbetween the two first fingerprints on the fingerprint sensor; andstoring the indication of plausible displacement for each of thepossible pairs to enable the electronic device to control authenticationof a user based on both: (i) the stored indications of plausibledisplacement between two first fingerprints, and (ii) second fingerprintdata of a subsequent hand using the fingerprint sensor.

In an aspect, there is provided a comparison method of authenticating auser using an electronic device comprising a fingerprint sensor. Themethod comprises: obtaining second fingerprint data of a subsequent handusing the fingerprint sensor; determining features of secondfingerprints of respective fingers of the subsequent hand; determining adegree of similarity between each one of a plurality of stored firstfingerprints from a first hand and each one of the second fingerprintsbased on a comparison of respective features of the first and secondfingerprints; identifying pairs of fingerprints, each pair comprisingone of the stored first fingerprints and the second fingerprint whichhas the highest degree of similarity to said one of the stored firstfingerprints; determining a plausibility of the displacement of eachpair relative to each of the other pairs based on a stored indication ofplausible displacement between possible pairs of the first fingerprints;and controlling authentication of the user based on the determinedplausibility of the displacement of each pair of fingerprints.

In an aspect, there is provided an electronic device comprising afingerprint sensor, which is capable of simultaneously acquiring aplurality of fingerprints of a hand, memory means and processing means,which are configured to implement any of the methods disclosed herein.

In an aspect, there is provided a computer program product, which isloadable in the memory means of an electronic device comprisingprocessing means and is designed to implement, when executed by saidprocessing means, any of the methods disclosed herein.

Aspects of the present disclosure are directed to methods toauthenticate a user in an electronic device comprising a fingerprintsensor, as well as to enrollment methods of preparing an electronicdevice comprising a fingerprint sensor to authenticate a user using thefingerprint sensor, and to comparison methods of authenticating a userusing an electronic device comprising a fingerprint sensor. Aspects arealso directed to an electronic device comprising a fingerprint sensorcapable of simultaneously acquiring a plurality of fingerprints of ahand, memory means, and processing means, which are configured toimplement any of the methods disclosed herein. Exemplary electronicdevices may comprise sensor arrays comprising a plurality of touchsensitive pixels.

For example, in an aspect there is provided a sensor array comprising aplurality of touch sensitive pixels, each pixel comprising: a capacitivesensing electrode for accumulating a charge in response to proximity ofa conductive object to be sensed; a reference capacitor connected inseries with the capacitive sensing electrode so that, in response to acontrol voltage, an indicator voltage is provided at the connectionbetween the reference capacitor and the capacitive sensing electrode toindicate the proximity of the conductive object to be sensed. Thisarrangement may reduce or overcome the problem associated with parasiticcapacitance which may occur in prior art touch sensors.

Each pixel may comprise a sense VCI (voltage-controlled impedance)having a control terminal connected so that the impedance of the senseVCI is controlled by the indicator voltage. Typically, the sense VCIcomprises at least one TFT (thin film transistor) and the conductionpath of the VCI comprises the channel of the TFT. A conduction path ofthe sense VCI may be connected to a first plate of the referencecapacitor, and the control terminal of the first VCI is connected to thesecond plate of the reference capacitor. At least one plate of thereference capacitor may be provided by a metallisation layer of a thinfilm structure which provides the sense VCI.

The conduction path of the sense VCI may connect the first plate of thereference capacitor, and so also the control voltage, to an input of areadout circuit. This may enable the circuitry which provides thecontrol voltage also to provide the basis for the output signal of thepixel. This may further address problems associated with parasiticcapacitance and signal to noise ratio in prior art touch sensors. Analternative way to address this same problem is to arrange theconduction path of the sense VCI to connect a reference signal supply toan input of a readout circuit. The reference signal supply may comprisea constant voltage current source. Thus, modulating the impedance of thesense VCI of a pixel controls the current from that pixel to the inputof the read-out circuit.

A select VCI may also be included in each pixel. This may be connectedso that its conduction path is connected in series between theconduction path of the sense VCI and the reference signal supply. Thus,switching the select VCI into a non-conducting state can isolate thesense VCI from the reference signal input, whereas switching the selectVCI into a conducting state can enable current to flow through the pixel(depending on the impedance of the sense VCI). A control terminal of theselect VCI may be connected for receiving the control voltage, e.g. fromagate drive circuit.

Each pixel may comprise a gate line VCI, and a conduction path of thegate line VCI may connect the reference signal supply to the first plateof the reference capacitor for providing the control voltage.

Each pixel may comprise a reset circuit for setting the control terminalof the sense VCI to a selected reset voltage. The reset circuit maycomprise a reset VCI. A conduction path of the reset VCI is connectedbetween a second plate of the reference capacitor and one of (a) a resetvoltage; and (b) a first plate of the reference capacitor. A controlterminal of the reset VCI may be connected to another pixel of thesensor for receiving a reset signal (e.g. from a channel of a gate drivecircuit which is connected to the control terminal of the select VCI ofa pixel in another row of the array). The reset signal may be configuredto switch the reset VCI into a conducting state, thereby to connect thesecond plate of the reference capacitor to the one of (a) the resetvoltage and (b) the first plate of the capacitor. Connecting the secondplate of the reference capacitor to the one of (a) the reset voltage.

The present disclosure will now be described with reference to theaccompanying drawings, wherein:

FIG. 1 shows an electronic device provided with a fingerprint sensor;

FIG. 2 shows an example of a fingerprint image acquired by theelectronic device of FIG. 1 ,

FIG. 3 shows a general flowchart of the method to authenticate a user inthe electronic device of FIG. 1 according to the invention;

FIG. 4 shows, more in detail, a part of the flowchart of FIG. 3concerning a fingerprint enrollment process which is part of the userauthentication method according to the invention;

FIG. 5 is a graphic representation of a mathematical model createdduring the enrollment process;

FIG. 6 shows, more in detail, a part of the flowchart of FIG. 3concerning a fingerprint comparison process which is part of the userauthentication method according to the invention;

FIG. 7 shows a graphic representation concerning a step of thecomparison process of FIG. 6 ;

FIG. 8 is a graphic representation similar to the one of FIG. 5 , buthere the mathematical model is updated after a plurality of executionsof the comparison process;

FIG. 9 comprises a plan view of a sensor apparatus comprising a sensorarray, and Inset A of FIG. 9 shows a circuit diagram for a pixel of thesensor array;

FIG. 10 shows a circuit diagram of a sensor array for a sensor apparatussuch as that illustrated in FIG. 9 ;

FIG. 11 shows a circuit diagram of another sensor array of the typeshown in FIG. 9 ;

FIG. 12 is a schematic diagram of a pixel circuit diagram of a top gatestructure of a pixel in a pixel array.

In FIG. 1 , number 1 generally indicates, as a whole, an electronicdevice, for example a smartphone, comprising a touchscreen 2, which isprovided with a built-in fingerprint sensor (which is not shown), atleast one memory 3 and a processing unit 4, which is interfaced with thetouchscreen 2 and the memory 3. The fingerprint sensor is built-in in atleast part of the area of the touchscreen 2 so as to simultaneouslyacquire a plurality of fingerprints of a hand without impacting thenormal operation of the touchscreen 2.

In FIG. 2 , number 5 indicates a fingerprint image of a hand of a useracquired by means of the fingerprint sensor built-in in the touchscreen2. The fingerprint image 5 comprises four fingerprints 6 belonging torespective fingers of the hand, namely all fingers except the thumb. Thefingerprint image 5 is acquired when the user grabs the smartphone 1laying the fingers, except the thumb, on the touchscreen 2.

The processing unit 4 is configured to implement the user authenticationmethod according to the invention. In particular, a computer programproduct is loaded in the memory 3 and is designed to implement, when itis executed by the processing unit 4, the user authentication methodaccording to the invention, said method being described hereinafter withparticular reference to FIGS. 3-5

With reference to FIG. 3 , the method to authenticate a user in theelectronic device 1 comprises, in general, an enrollment process (step100), which is typically carried out only once when the electronicdevice 1 is used for the first time, and a comparison process (step200), which is typically carried out each time the user wants to use theelectronic device 1, for example to unlock the touchscreen 2.

With reference to FIG. 4 , the enrollment process involves acquiring afirst fingerprint image of a hand, like the one shown in FIG. 2 andindicated hereinafter with E, by means of the fingerprint sensor (step101) and processing the fingerprint image E in order to determinefeatures of first fingerprints, hereinafter generically indicated withEi (i≤5), belonging to respective fingers of the hand.

In particular, the fingerprint image E is processed and, in particular,segmented according to known techniques in order to isolate the singlefingerprints Ei (step 102) and, then, each fingerprint Ei is processedso as to extract its minutiae, namely ending and bifurcations of theridges characterizing the fingerprints, and a respective reference pointRi is determined based on the minutiae (step 103). Therefore, thefeatures of a fingerprint Ei comprise the respective minutiae andreference point Ri.

Both the minutiae and the respective reference point Ri are identifiedrelative to a coordinate system on a surface along which the fingerprintimage E develops. In the example of FIGS. 1 and 2 , the surface of thefingerprint image is a plane.

The reference point Ri advantageously consists of the geometric centreof mass of the minutia of the fingerprint Ei.

Furthermore, for each one of the possible pairs of first fingerprints,hereinafter indicated with <Ei,Ej> (i≤5; j≤5; i<j), a respectivevariation model Gij indicating a plausible variation of the mutualposition of the two fingerprints Ei and Ej is initialized based on therespective features (step 104). In particular, the variation model Gijis defined by a bivariate Gaussian function having a mean vector μijcalculated as a function of the features of the corresponding pair offingerprints <Ei,Ej> and a covariance matrix Σij. The mean vector μij isinitialized to an initial displacement vector D0 ij, which is calculatedas a difference vector between the reference points Ri and Rj of thepair of fingerprints <Ei,Ej>, namely:D0ij=Rj−Ri  (1)

and the covariance matrix Σij is initialized to a scalar diagonalmatrix, i.e. a matrix with values along the diagonal that are equal toan initial value Σ0 representing an initial position tolerance.

The minutiae and the reference points Ri of all fingerprints Ei as wellas the variation models Σij of the possible pairs of fingerprints<Ei,Ej> are stored in an enrollment template in the memory 3 for theexecution of the following comparison process (step 105).

FIG. 5 is a graphic representation of the variation model Gij relativeto a Cartesian coordinate system at the end of the enrollment process.The ellipses drawn with a dashed line represent the areas of twofingerprints Ei and Ej, whose respective reference points Ri and Rj arethe ends of the initial displacement vector D0 ij, and the circles drawnwith a continuous line represent the iso-probability contours of thevariation model Gij, which is defined by a bivariate Gaussian functioncentred on the mean value μij and having a spherical symmetry defined bythe covariance matrix Σij.

With reference to FIG. 6 , the comparison process, similarly to theenrollment process, involves acquiring a successive fingerprint image ofa hand, like the one shown in FIG. 2 and indicated hereinafter with C,by means of the fingerprint sensor (step 201) and processing thefingerprint image C in order to determine features of successivefingerprints, hereinafter generically indicated with Ck (k≤5), belongingto respective fingers of the hand. The features considered for thefingerprints Ck are its minutiae.

The fingerprint image C is processed and, in particular, segmentedaccording to known techniques so as to isolate the single fingerprintsCk (step 202) and, then, each fingerprint Ck is processed to extract itsminutiae (203). The minutiae of the fingerprints Ck are identifiedrelative to the same coordinate system used to identify the minutiae ofthe fingerprints Ei.

At this point, a degree of similarity is determined between each one ofthe fingerprints Ei and each one of the fingerprints Ck based on acomparison of the respective features (204). In particular, the minutiaeof each fingerprint Ei are compared with the minutiae of each one of thefingerprints Ck, using known comparison techniques, in order todetermine, for each comparison and, hence, for each pair of fingerprints<Ei,Ck>, a respective confidence score S and a respectiverototranslation. The confidence score S is, for example, proportional tothe number of coincident minutiae in the two fingerprints Ei and Ck. Therototranslation consists of a rotation angle θ and of a translationvector T, which maximize the number of matchings between minutiae of thetwo fingerprints Ei and Ck. In other words, the degree of similarity ofa pair of fingerprints <Ei,Ck> is defined by the respective confidencescore Sand by the respective rototranslation, hereinafter syntheticallyindicated with (θi,Ti).

A set P of pairs of fingerprints is populated by selecting, for eachfingerprint Ei, the fingerprint Cki offering the highest degree ofsimilarity and, in particular, the highest confidence score S (step205). Hereafter, ki must be intended as a single index which spans theset of fingerprints C and is in correspondence with the index i spanningthe set of fingerprints E.

Therefore, the set P can be represented as follows:P={<Ei,Cki>};i≤5;ki≤5.

Owing to the above, each pair <Ei,Cki> of the set P is associated with arespective confidence score Sand with a respective rototranslation(θi,Ti).

The set P is advantageously reduced by removing those pairs offingerprints <Ei,Cki> whose confidence score S is smaller than athreshold value Sth1 (step 206). In other words, the set P comprisespairs of fingerprints <Ei,Cki> whose confidence score S is greater thanor equal to the threshold value Sth1. The threshold value Sth1 is chosenbased on the desired degree of safety of the authentication method andon the type of algorithm used for the comparison.

The comparison process goes on by determining a degree of plausibilityof the mutual position of each pair of fingerprints <Ei,Cki> relative toevery other pair of fingerprints <Ej,Ckj> of the set P, so that j>i,based on the features of the fingerprints Ei, Cki, Ej and Ckj of the twopairs of fingerprints <Ei,Cki> and <Ej,Ckj> and based on the variationmodel Gij concerning the fingerprints Ei and Ej and the aforesaid twopairs of fingerprints.

In particular, a displacement vector Dij of each pair <Ei,Cki> relativeto every other pair <Ej,Ckj> of the set P is computed based on thereference points Ri and Rj of the fingerprints Ei and Ej of the twopairs <Ei,Cki> and <Ej,Ckj> and based on the rototranslations (θi,Ti)and (θj,Tj) of the two pairs <Ei,Cki> and <Ej,Ckj>, namely based on therototranslation (θi,Ti) between the fingerprints Ei and Cki and therototranslation (θj,Tj) between the fingerprints Ej and Ckj (step 207).

To this aim, the respective rototranslations (θi,Ti) and (θj,Tj) areapplied to the reference points Ri and Rj of the fingerprints Ei and Ejof the two pairs <Ei,Cki> and <Ej,Ckj> in order to obtain two respectivenew reference points, hereinafter indicated with Ri′ and Rj′. Thedisplacement vector Dij is computed as rotation of a difference vectorbetween the two new reference points Ri′ and Rj′, this rotation beingequal to an average θavg of the rotation angles θi and θj of therototranslations (θi,Ti) and (θj,Tj). More synthetically, thedisplacement vector Dij is obtained from a function of the followingtype:Dij=rotate(Rj′−Ri′,θavg).  (2)

FIG. 7 is a graphic representation of an example of two fingerprints Eiand Ej mapped on the corresponding fingerprints Cki and Ckj, showing theposition of the new reference points Ri′ and Rj′. The fingerprints Eiand Ej and the corresponding fingerprints Cki and Ckj only partiallyoverlap because the fingerprint portions of the same fingers captured indifferent acquisitions are not necessarily the same. In other words, thedisplacement vector Dij cannot be calculated starting from possiblereference points of the fingerprints Cki and Ckj, because these pointswould not necessarily correspond to the original reference points of thefingerprints Ei and Ej of the same fingers. Indeed, since the referencepoints are calculated based on the visible minutiae of the fingerprintimage, the position of the minutiae in the image depends on thefingerprint portions that come into contact with the touchscreen 2.Furthermore, the displacement vector Dij must be rotated to thecoordinates of the initial displacement vector D0 ij, where thevariation model Gij is defined.

At this point, a probability density of observing the displacementvector Dij, hereinafter indicated with p(Dij), is computed based on thevariation model Gij concerning the fingerprints Ei and Ej of the twopairs <Ei,Cki> and <Ej,Ckj> (step 208). In particular, the probabilitydensity p(Dij) is computed taking into account a probability densitywith a bivariate Gaussian distribution, which has a mean vector and acovariance matrix coinciding with the mean vector μij and with thecovariance matrix Σij, respectively, of the variation model Gijconcerning the fingerprints Ei and Ej of the two pairs <Ei,Cki> and<Ej,Ckj>, namely with the formula:

$\begin{matrix}{{p({Dij})} = {\frac{1}{2\pi{{\sum{ij}}}^{1/2}}e^{- \frac{{({{Dij} - {\mu\;{ij}}})}^{t}{\sum{{ij}^{- 1}{({{Dij} - {\mu\;{ij}}})}}}}{2}}}} & (3)\end{matrix}$

wherein |Σij| and Σij⁻¹ are the determinant and the inverse matrix,respectively, of the covariance matrix Σij. Therefore, the degree ofplausibility of the mutual position of each pair <Ei,Cki> relative toevery other pair <Ej,Ckj> of the set P is defined by the probabilitydensity p(Dij).

The user recognition takes place based on the degrees of similaritybetween the fingerprints Ei and the fingerprints Cki and based on thedegrees of plausibility of the mutual position of the pairs <Ei,Cki> and<Ej,Ckj> of the set P.

In particular, an initial global score ST1 is computed by summing theconfidence scores S associated with the pairs <Ei,Cki> and <Ej,Ckj> ofthe set P (step 209). A final global score ST2 is computed byconsolidating the initial global score ST1 according to the probabilitydensities p(Dij) of the displacement vectors Dij between the pairs<Ei,Cki> and <Ej,Ckj> of the set P (step 210). The two fingerprintimages E and C are assumed to belong to the same user and, hence, theuser is recognized, if the final global score ST2 is greater than asecond threshold value Sth2 (step 211). The threshold value Sth2 ischosen based on the desired degree of safety of the authenticationmethod.

The consolidation of the initial global score ST1 takes place through afunction of the probability density p(Dij), which depends on theparticular electronic device 1, namely on the specific field of usethereof. Generally speaking, if the degrees of plausibility of themutual position of the pairs of fingerprints of the set Pare high, thenthe global score is increased, namely the final global score ST2 becomeshigher than the initial global score ST1, vice versa if the mutualposition plausibility degrees are low. In other words, if the mutualpositions of the fingerprints Ck are consistent relative to thevariation models Gij, then the global score needs to be increasedproportionally to the consistency level.

According to a further embodiment of the invention which is not shownherein, the first global score ST1 is calculated immediately after thefinal formation of the set P, namely after step 206 of FIG. 5 .

Finally, all variation models Gij are updated based on the degrees ofsimilarity and on the mutual position plausibility degrees. Inparticular, the variation models Gij are updated if the global score ST2exceeds a third threshold value Sth3 (step 212). The threshold valueSth3 is chosen based on the desired degree of safety of theauthentication method, as well.

In particular, for each variation model Gij concerning the fingerprintsEi and Ej, the respective mean vector μij and the respective covariancematrix Σij are updated according to the displacement vector Dij of thosepairs <Ei,Cki> and <Ej,Ckj> of the set P comprising the fingerprints Eiand Ej.

For example, the displacement vectors Dij of a given number of lastsuccessive fingerprint images C are stored in the memory 3: the meanvector μij is recomputed as average of said number of last displacementvectors Dij and the covariance matrix Σij is recomputed based on saidnumber of last displacement vectors Dij and on the mean vector μijaccording to known formulas. According to an alternative example, themean vector μij and the covariance matrix Σij are recomputed with knownincremental formulas based on the displacement vector Dij of the lastfingerprint image C acquired.

FIG. 8 is an example graphic representation, similar to the one of FIG.5 , of the variation model Gij updated after a plurality of executionsof the comparison process concerning the acquisition of a plurality ofsuccessive fingerprint images C and, hence, of a plurality of successivefingerprints. The ellipses drawn with a dashed lines and indicated withEj are the areas of fingerprints of the same finger after a plurality ofupdates. The variation model Gij of the example shown herein hasiso-probability contours shaped like ellipses, which means that it isupdated to tolerate great displacements along a direction that istransversal to the longitudinal axes of the outlines of thefingerprints.

Even though the invention described above relates to a specificembodiment, it should not be considered as limited to said embodiment,for its scope of protection also includes all those variants, changes orsimplifications covered by the appended claims, such as for example:

-   -   using a probability model other than the Gaussian to determine        the degree of plausibility, for example the use of a mixture of        Gaussian would enable to learn touch modalities which can be        quite different each from the other;    -   using features other than the minutiae and, hence, different        comparison techniques to determine the degree of similarity        between the fingerprints Ei and Ck, for example the patch        correlation technique.

Moreover, the user authentication method according to the invention isapplicable also to electronic devices having a fingerprint sensorembedded in a support which is not planar as a conventional touchscreen,such as a door handle of a vehicle or room or the helve of a weapon, sothat the user can be authenticated before to open the door or use theweapon.

The main advantage of the user authentication method according to theinvention is that of recognizing the user not only based on the featuresextracted from the fingerprints acquired by the fingerprint sensor, butalso based on finger anatomic information implicitly obtained from themutual position of the different fingerprints of a hand, thanks to thevariation model determined in an enrollment process and to the degree ofplausibility of the mutual position of the fingerprints determined basedon the variation model during a following comparison process. Indeed,even if the mutual positions of the fingerprints of the fingers of ahand can change in successive acquisitions of fingerprint images, due tothe different ways in which the user grabs the electronic device 1 andtouches the touch screen 2, the anatomy of the hand limits the degree offreedom of the movements of the fingers and, hence, the variability ofthe mutual position of the fingers.

It is to be appreciated in the context of the present disclosure thatwhile reference may have been made to a sensor which can simultaneouslyobtain fingerprint data for multiple fingerprints, this is notnecessary. The systems and methods provide means for processing multiplefingerprint data, but these are not limited to specific temporalcircumstances (e.g. the time at which the fingerprint data wasobtained). For example, embodiments may find equal utility for sensorswhere fingerprint data has been obtained sequentially for eachfingerprint.

It is to be appreciated in the context of the present disclosure thatwhile reference has been made to memory means of the electronic device,this memory may be provided by a component external to the electronicdevice. For example, the memory may be cloud based. The electronicdevice may communicate with a third party component which storesrelevant data. This communication may be wired or wireless.

It is to be appreciated in the context of the present disclosure thatwhile reference has been made to a variation model (Gij), explicit useof any such model is not necessary. The variation model may provide anindication of an expected position of each of a user's fingerprints(e.g. including an orientation) and/or an expected displacement betweeneach of a user's fingerprints. Based on the expectedposition/displacement of a user's fingerprints, it may be determinedwhether their fingerprints correspond to those of the authenticateduser. It will therefore be appreciated that use of a specific model isnot explicitly required to perform this function.

For example, data may be stored which indicates expected bounds forposition/displacement of fingerprints. Any sensed fingerprint dataoutside these bounds may be deemed to not belong to the user. As anotherexample, data may be stored which corresponds to (e.g. is) data obtainedfrom the verified user. A comparison may then be performed betweensubsequently obtained data and this stored data to determine whether thesubsequently obtained data also corresponds to data for theauthenticated user. This comparison may be performed withoutinitializing (and also storing) a variation model. Authenticationdeterminations may be performed on-the-fly based on stored data, withoutthe need for a variation model.

It is to be appreciated in the context of the present disclosure thatwhile reference has been made to populating a set of pairs, this may notbe necessary. Each fingerprint from the first hand may be associatedwith a fingerprint from the successive hand. Calculations may beperformed using these two fingerprints and comparisons between them andother fingerprint combinations between first and successive hands. Pairsneed not be populated to perform this desired functionality. Forexample, calculations may be made on-the-fly

It is to be appreciated in the context of the present disclosure thatwhile methods described herein include both an enrollment process and acomparison process, these may be performed separately, e.g. each may beperformed without the other. For example, the comparison process may beperformed, and achieve desired technical effects, using stored data. Thestored data could have been obtained using an enrollment process asdescribed herein, or it could have been obtained in another manner, suchas from another source of fingerprint data. Likewise, the enrollmentprocess may occur without the ensuing comparison process. This may stillenable a technical effect of enabling identification of a user based onmutual location of their fingerprints.

It is to be appreciated in the context of the present disclosure thatwhile methods described herein include updating variation models basedon degrees of similarity and degrees of plausibility of the mutualposition, it is to be appreciated that this is not necessary.Embodiments may still enable a user to be identified based on a mutualposition of their fingerprints without updating variation models. Oncethe variation model has been initialized, the methods may still functionto provide desired technical effects without updating the variationmodel. It is also to be appreciated that determining the degree ofplausibility of the mutual position of each pair may occur without beingbased on the specific features of each fingertip. For example, arelative position may still be sensed without knowledge of the featureof the fingertip. Recognizing the user may not need to be based on boththe degree of similarity between fingerprints and degrees ofplausibility of mutual position. It may just be based on degrees ofplausibility of mutual position. This may still enable a user to beidentified based on the relative displacement between theirfingerprints. Likewise, it is to be appreciated that a mutual positionof each successive fingertip is not required. The user may be identified(e.g. as the authenticated user, or not the authenticated user) based onmutual position of first fingerprints alone.

Methods of the present disclosure may be implemented by any suitableelectronic device comprising a fingerprint sensor. It is to beappreciated in the context of the present disclosure that any suchsuitable electronic device may be provided. Such electronic devices maycomprise a sensor apparatus. Such a sensor apparatus may comprise aplurality of touch sensitive pixels. Exemplary sensor apparatuses andpixels shall now be described which may be configured to provide thefunctionality of the methods disclosed herein.

FIG. 9 shows a sensor apparatus 2001 in which the sensor array 2010 ofthe present disclosure may be incorporated. FIG. 10 illustrates acircuit diagram of one such sensor array 2010. The description whichfollows shall refer to FIG. 9 and FIG. 10 together. It can be seen froman inspection of FIG. 9 and FIG. 10 that inset A of FIG. 9 shows adetailed view of one pixel of this array 2010.

The sensor array 2010 comprises a plurality of touch sensitive pixels2012. Typically, other than in respect of its position in the array,each pixel 2012 is identical to the others in the array 2010. Asillustrated, each pixel 2012 comprises a capacitive sensing electrode2014 for accumulating a charge in response to proximity of the surfaceof a conductive object to be sensed. A reference capacitor 2016 isconnected between the capacitive sensing electrode 2014 and a connectionto a gate drive channel 2024-1 of a gate drive circuit 2024. Thus, afirst plate of the reference capacitor 2016 is connected to the gatedrive channel 2024-1, and a second plate of the reference capacitor 2016is connected to the capacitive sensing electrode 2014.

Each pixel 2012 may also comprise a sense VCI (voltage-controlledimpedance) 2020 having a conduction path, and a control terminal (2022;inset A, FIG. 9 ) for controlling the impedance of the conduction path.The conduction path of the sense VCI 2020 may connect the gate drivechannel 2024-1 to an output of the pixel 2012. The control terminal 2022of the VCI is connected to the capacitive sensing electrode 2014 and tothe second plate of the reference capacitor 2016. Thus, in response to acontrol voltage applied by the gate drive channel 2024-1, the referencecapacitor 2016 and the capacitive sensing electrode 2014 act as acapacitive potential divider.

The capacitance of the capacitive sensing electrode 2014 depends on theproximity, to the capacitive sensing electrode 2014, of a conductivesurface of an object to be sensed. Thus, when a control voltage isapplied to the first plate of the reference capacitor 2016, the relativedivision of that voltage between that sensing electrode 2014 and thereference capacitor 2016 provides an indication of the proximity of thesurface of that conductive object to the capacitive sensing electrode2014. This division of the control voltage provides an indicator voltageat the connection 2018 between the reference capacitor 2016 and thecapacitive sensing electrode 2014. This indicator voltage can be appliedto the control terminal 2022 of the sense VCI 2020 to provide an outputfrom the pixel 2012 which indicates proximity of the conductive object.

Pixels may be positioned sufficiently close together so as to be able toresolve contours of the skin such as those associated with epidermalridges, for example those present in a fingerprint, palmprint or otheridentifying surface of the body. It will be appreciated in the contextof the present disclosure that contours of the skin may comprise ridges,and valleys between those ridges. During touch sensing, the ridges maybe relatively closer to a sensing electrode than the “valleys” betweenthose ridges. Accordingly, the capacitance of a sensing electrodeadjacent a ridge will be higher than that of a sensing electrode whichis adjacent a valley. The description which follows explains how systemscan be provided in which sensors of sufficiently high resolution toperform fingerprint and other biometric touch sensing may be providedover larger areas than has previously been possible.

As shown in FIG. 9 and FIG. 10 in addition to the sensor array 2010,such a sensor may also comprise a dielectric shield 2008, agate drivecircuit 2024, and a read-out circuit 2026. A connector 2025 forconnection to a host device may also be included. This may be providedby a multi-channel connector having a plurality of conductive lines.This may be flexible, and may comprise a connector such as a flexi, orflexi-rigid PCB, a ribbon cable or similar. The connector 2025 may carrya host interface 2027, such as a plug or socket, for connecting theconductive lines in the connector to signal channels of a host device inwhich the sensor apparatus 2001 is to be included.

The host interface 2027 is connected by the connector 2025 to theread-out circuit 2026. A controller (2006; FIG. 10 ) may be connected tothe gate drive circuit 2024 for operating the sensor array, and to theread-out circuit 2026 for obtaining signals indicative of theself-capacitance of pixels of the sensor array 2010.

The dielectric shield 2008 is generally in the form of a sheet of aninsulating material which may be transparent and flexible such as apolymer or glass. The dielectric shield 2008 may be flexible, and may becurved. An ‘active area’ of this shield overlies the sensor array 2010.In some embodiments, the VCIs and other pixel components are carried ona separate substrate, and the shield 2008 overlies these components ontheir substrate. In other embodiments the shield 2008 provides thesubstrate for these components.

The sensor array 2010 may take any one of the variety of forms discussedherein. Different pixel designs may be used, typically however thepixels 2012 comprise at least a capacitive sensing electrode 2014, areference capacitor 2016, and at least a sense VCI 2020.

The array illustrated in FIG. 10 comprises a plurality of rows of pixelssuch as those illustrated in FIG. 9 . Also shown in FIG. 10 is the gatedrive circuit 2024, the read out circuit 2026, and a controller 2006.The controller 2006 is configured to provide a clock signal, e.g. aperiodic trigger, to the gate drive circuit 2024, and to the read-outcircuit 2026.

The gate drive circuit 2024 comprises a plurality of gate drive channels2024-1, 2024-2, 2024-3, which it is operable to control separately, e.g.independently. Each such gate drive channel 2024-1, 2024-2, 2024-3comprises a voltage source arranged to provide a control voltage output.And each channel 2024-1 is connected to a corresponding row of pixels2012 of the sensor array 2010. In the arrangement shown in FIG. 10 eachgate drive channel 2024-1, 2024-2, 2024-3 is connected to the firstplate of the reference capacitor 2016 in each pixel 2012 of its row ofthe sensor array 2010. During each clock cycle, the gate drive circuit2024 is configured to activate one of the gate drive channels 2024-1,2024-2, 2024-3 by applying a gate drive pulse to those pixels. Thus,over a series of cycles the channels (and hence the rows) are activatedin sequence, and move from one step in this sequence to the next inresponse to the clock cycle from the controller 2006.

The read-out circuit 2026 comprises a plurality of input channels2026-1, 2026-2, 2026-3. Each input channel 2026-1, 2026-2, 2026-3 isconnected to a corresponding column of pixels 2012 in the sensor array2010. To provide these connections, the conduction path of the sense VCI2020 in each pixel 2012 is connected to the input channel 2026-1 for thecolumn.

Each input channel 2026-1, 2026-2, 2026-3 of the read out circuit 2026may comprise an analogue front end (AFE) and an analogue-to-digitalconverter (ADC) for obtaining a digital signal from the column connectedto that input channel 2026-1. For example it may integrate the currentapplied to the input channel during the gate pulse to provide a measureof the current passed through the sense VCI 2020 of the active pixel2012 in that column. The read out circuit 2026 may convert this signalto digital data using the ADC. Furthermore, the analogue front endperforms impedance matching, signal filtering and other signalconditioning and may also provide a virtual reference.

In the sensor array 2010 shown in FIG. 10 , the conduction channel ofthe sense VCI 2020 in each pixel connects the input channel of the readout circuit for that column to the gate drive channel for the pixel'srow. In FIG. 10 , the gate drive channel for the row thus provides areference input. Operation of the sense VCI 2020 modulates thisreference input to provide the pixel output. This output signal from apixel indicates the charge stored on the capacitive sensing electrode2014 in response to that reference input relative to that stored on thereference capacitor.

FIG. 9 includes a grid as a very schematic illustration of the rows andcolumns of pixels 2012 which make up the array. Typically this will be arectilinear grid, and typically the rows and columns will be evenlyspaced. For example the pixels may be square. It will of course beappreciated that the grid shown in FIG. 9 is not to scale. Typically thesensor array has a pixel spacing of at least 200 dots per inch, dpi (78dots per cm). The pixel spacing may be at least 300 dpi (118 dots percm), for example at least 500 dpi (196 dots per cm).

Operation of the sensor array 2010 of FIG. 10 will now be described.

On each cycle of operation, the gate drive circuit 2024 and the read outcircuit 2026 each receive a clock signal from the controller 2006.

In response to this clock signal, the gate drive circuit operates one ofthe gate drive channels to apply a control voltage to one of the rows ofthe array. In each pixel in the row, the control voltage from the gatedrive channel is applied to the series connection of the referencecapacitor 2016 and the capacitive sensing electrode 2014. The voltage atthe connection 2018 between the two provides an indicator voltageindicating the proximity of a conductive surface of an object to besensed to the capacitive sensing electrode 2014. This indicator voltagemay be applied to the control terminal of the sense VCI 2020 to controlthe impedance of the conduction path of the sense VCI 2020. A current isthus provided through the conduction path of the sense VCI 2020 from thegate drive to the input channel for the pixel's column. This current isdetermined by the gate drive voltage, and by the impedance of theconduction channel.

In response to the same clock signal, the read-out circuit 2026 sensesthe pixel output signal at each input channel. This may be done byintegrating the current received at each input of the read-out circuit2026 over the time period of the gate pulse. The signal at each inputchannel, such as a voltage obtained by integrating the current from thecorresponding column of the array, may be digitised (e.g. using an ADC).Thus, for each gate pulse, the read-out circuit 2026 obtains a set ofdigital signals, each signal corresponding to a column of the active rowduring that gate pulse. So the set of signals together represent theactive row as a whole, and the output from each pixel being indicativeof the charge stored on and/or the self-capacitance of the capacitivesensing electrode 2014 in that pixel.

Following this same process, each of the gate-drive channels isactivated in sequence. This drives the sense VCI 2020 of each pixelconnected to that channel into a conducting state for a selected time(typically the duration of one gate pulse). By activating the rows ofthe array in sequence the read out circuit, can scan the sensor arrayrow-wise. Other pixel designs, other scan sequences, and other types ofsensor array, may be used.

FIG. 11 illustrates another sensor array which may be used in theapparatus illustrated in FIG. 9 .

FIG. 11 shows a sensor array 2010 comprising a plurality of pixels, anda reference signal supply 2028 for supplying a reference signal to thepixels. This can avoid the need for the gate drive power supply also toprovide the current necessary for the read-out signal.

Also shown in FIG. 11 is the gate drive circuit 2024, the read-outcircuit 2026, and the controller 2006.

The sensor array 2010 may also benefit from the inclusion of a resetcircuit 2032, 2034 in each pixel. This may allow the control terminal2022 of the pixel to be pre-charged to a selected reset voltage whilstthe pixel is inactive (e.g. while another row of the array is beingactivated by the application of a gate pulse to another, different, rowof the array).

In these embodiments the sensor may also comprise a reset voltageprovider 2042 for providing a reset voltage to each of the pixels 2012of the array as described below. The reset voltage provider 2042 maycomprise voltage source circuitry, which may be configured to provide acontrollable voltage, and may be connected to the controller 2006 toenable the controller 2006 to adjust and fix the reset voltage.

The reset voltage may be selected to tune the sensitivity of the pixel.In particular, the output current of the sense VCI 2020 typically has acharacteristic dependence on the indicator voltage at the controlterminal 2022 and its switch-on voltage. Thus the reset voltage may bechosen based on the switch-on voltage of the sense VCI 2020. Thecharacteristic may also comprise a linear region in which it may bepreferable to operate.

The pixels illustrated in FIG. 11 are similar to those illustrated inFIG. 9 and FIG. 10 in that each comprise a capacitive sensing electrode2014, and a reference capacitor 2016 connected with a capacitive sensingelectrode 2014. The connection between these two capacitances providesan indicator voltage, which can for example be connected to the controlterminal 2022 of a sense VCI 2020. In addition, the pixels of the sensorarray illustrated in FIG. 11 also comprise a further two VCIs 2034,2038, and a connection to the reset voltage provider 2042, and aconnection to the reference signal supply 2028.

As noted above, the sense VCI 2020 is arranged substantially asdescribed above with reference to FIG. 9 , in that its control terminal2022 is connected to the connection between the reference capacitor 2016and the capacitive sensing electrode 2014. However, the conduction pathof the sense VCI 2020 is connected differently in FIG. 11 than in FIG. 9. In particular, the conduction channel of the select VCI 2038 connectsthe conduction channel of the sense VCI 2020 to the reference signalsupply 2028 which provides a voltage V_(ref). Thus, the conductionchannel of the sense VCI 2020 is connected in series between theconduction channel of the select VCI 2038 and the input of the read-outcircuit for the column. The select VCI 2038 therefore acts as a switchthat, when open, connects the sense VCI 2020 between, V_(ref), thereference signal supply 2028 and the input of the read-out circuit and,when closed, disconnects the sense VCI from the reference signal supply2028. In the interests of clarity, the connection between the conductionchannel of the select VCI and V_(ref), the output of the referencesignal supply 2028 is shown only in the top row of the array of pixels.The connection reference signal supply 2028 in the lower rows of thearray is indicated in the drawing using the label V_(ref).

The select VCI 2038 is therefore operable to inhibit the provision ofsignal from any inactive pixel to the input of the read-out circuit2026. This can help to ensure that signal is only received from activepixels (e.g. those in the row to which the gate drive pulse is beingapplied).

In an embodiment each column of pixels is virtually connected to aground or reference voltage. As such there may be no voltage differenceson each of the columns thereby minimising parasitic capacitance.Furthermore, the reference signal supply may apply a current-driverather than a voltage-drive which further reduces any effect parasiticcapacitance could have on the signal applied by the active pixels on theinputs of the read-out circuit 2026.

The gate drive channel for the pixel row is connected to the first plateof the reference capacitor 2016, and to the control terminal of a selectVCI 2038. As in the pixel illustrated in FIG. 9 and FIG. 10 , theconnection to the reference capacitor 2016 and capacitor sensingelectrode 2014 means that the gate drive voltage is divided between thereference capacitor 2016 and the capacitive sensing electrode 2014 toprovide the indicator voltage which controls the sense VCI 2020. Theconnection to the control terminal 2040 of the select VCI 2038 howevermeans that, when the pixel is not active, the conduction path of thesense VCI 2020 is disconnected from the reference signal supply 2028.

A control terminal 2022 of the sense VCI 2020 is connected to the secondplate of the reference capacitor 2016. The conduction path of the senseVCI 2020 connects the reference signal supply 2028 to the input of theread-out circuit 2026 for the pixel's column.

A conduction path of the reset VCI 2034 is connected between the secondplate of the reference capacitor 2016 and a voltage output of the resetvoltage provider, for receiving the reset voltage. The control terminal2032 of the reset VCI 2034 is connected to a reset signal provider, suchas the gate drive channel of another row of the sensor array. This canenable the reset VCI 2034 to discharge the reference capacitor 2016during activation of another row of the array (e.g. a row of the arraywhich is activated on the gate pulse prior to the pixel's row) or topre-charge the control terminal 2022 of the sense VCI 2020 to the resetvoltage.

Operation of the sensor array of FIG. 11 will now be described.

The gate drive circuit 2024 and the read-out circuit 2026 each receive aclock signal from the controller 2006. In response to this clock signal,the gate drive circuit 2024 activates a first gate drive channel of thegate drive circuit 2024 to provide agate pulse to a row of the array2010. A control voltage is thus applied to the control terminal of theselect VCI 2038 of the pixels in the first row (the active row duringthis gate pulse).

The control voltage is also applied to the control terminal of the resetVCI 2034 of the pixels in a second row (inactive during this gatepulse).

In the first row (the active row), the conduction channel of the selectVCI 2038 is switched into a conducting state by the control voltage(e.g. that which is provided by the gate pulse). The conduction channelof the select VCI 2038 thus connects the conduction channel of the senseVCI 2020 to the reference signal supply 2028.

The control voltage is also applied to the first plate of the referencecapacitor 2016. The relative division of voltage between the sensingelectrode 2014 and the reference capacitor 2016 provides an indicatorvoltage at the connection between the reference capacitor 2016 and thecapacitive sensing electrode 2014 as described above with reference toFIG. 9 and FIG. 10 . The indicator voltage is applied to the controlterminal 2022 of the sense VCI 2020 to control the impedance of theconduction channel of the sense VCI 2020. Thus, the sense VCI 2020connects the reference signal supply 2028 to the input channel of theread-out circuit 2026 for that column, and presents an impedance betweenthe two which indicates the capacitance of the capacitive sensingelectrode 2014. Please note, the reference signal supply may be providedby a constant voltage current supply.

A current is thus provided through the conduction path of the sense VCI2020 from the reference signal supply 2028 to the input channel of theread-out circuit 2026 for the pixel's column. This current is determinedby the voltage of the reference signal supply and by the impedance ofthe conduction channel of the sense VCI.

In response to the same clock signal from the controller 2006, theread-out circuit 2026 senses the pixel output signal at each inputchannel (e.g. by integrating the current provided to each inputchannel), and digitises this signal. The integration time of theread-out circuit 2026 may match the duration of the gate pulse.

Thus, in each clock cycle, the read-out 2026 circuit obtains a set ofdigital signals, each signal corresponding to the signals sensed fromeach column of the active row during the gate pulse. The output fromeach pixel 2012 in the row (each channel during that gate pulse) beingindicative of the charge stored on the capacitive sensing electrode inthat pixel.

In the second (inactive) row the control voltage is applied to thecontrol terminal 2032 of the reset VCI 2034. This causes the reset VCI2034 of the pixels in the inactive row to connect the second plate oftheir reference capacitors 2016 to a reset voltage provided by the resetvoltage provider. This may discharge (e.g. at least partially remove)charge accumulated on the pixels of the inactive row, or it may chargethem to the reset voltage, before they are next activated in asubsequent gate pulse. This reset voltage may be selected to tune thesensitivity of the pixels.

At the boundaries of the pixel array, where an N−1 gate line is notavailable, a dummy signal may be used to provide the control signal tothe reset VCI. The gate drive circuit 2024 may provide the dummy signal.This may be provided by agate drive channel which is only connected tothe rest VCIs of a row at the boundary of the array, but not to anysense or select VCIs.

As illustrated in FIG. 11 , the reset VCI 2034 of the pixels may beconnected to the gate drive circuit so that each row is discharged inthis way by the gate pulse which activates the immediately precedingrow, which may be an adjacent row of the array.

In other examples, a reference capacitor need not be provided. FIG. 12illustrates one example pixel circuit in which a reference capacitor isnot provided. The circuit comprises a TFT 3030, 3032, 3038, and acapacitive sensing electrode 3014. The pixel circuit may be addressed byagate line 3027 and a source-data line 3028, and outputs to a commonline, for example a Vcom connection. The TFT comprises a source region3030, a drain region 3032 and a gate electrode 3038. The gate line 3027is connected to the gate electrode 3038. The source region 3030 isconnected to the source-data line 3028. The capacitive sensing electrodeis connected to the drain region 3032, which is connected to the sourceregion 3030, as shown in FIG. 12 .

The example pixel circuit of FIG. 12 may be provided by a layered pixelstructure. For example, the layered pixel structure may comprise threeconductive layers m1, m2, m3. These may be metallisation layers. A firstmetallization layer m1 provides the capacitive sensing electrode 3014.The first metallization layer m1 may be deposited on a carriersubstrate, such as a dielectric shield. A second metallisation layer,m2, provides the source 3030 and drain 3032 region of the TFT. Thesecond layer m2 may be the type as would be provided in a top gatearrangement. A third metallisation layer, m3, provides the gateelectrode 3038. In a bottom gate configuration, second and thirdmetallisation layers may be reversed. A conductive via may be providedto provide an electrical connection between the capacitive sensingelectrode 3014 and the drain region 3032 of the TFT, as can be seen inFIG. 12 .

As illustrated in FIG. 12 , the deposited metal layers denoted as m1, m2and m3 adjacent the features of the circuit in FIG. 12 can be connectedto form the circuit. The illustrated circuit components of the circuitdiagram in FIG. 12 may depict both top gate and bottom gatearrangements. A top gate configuration is illustrated in FIG. 12 , butit will be appreciated that m2 and m3 can be swapped in order tocorrespond to a bottom gate configuration.

In some examples, a reference capacitor could be included in the pixelcircuit of FIG. 12 . The reference capacitor may be connected to thedrain region 3032. For example, one of the plates of the referencecapacitor may be provided by the second metallisation layer. A secondplate of the reference capacitor may also be provided by the thirdmetallisation layer. The second plate of the reference capacitor may beseparated from the gate electrode 3038, for example by patterning (e.g.lithography or etching) during manufacture.

It will be appreciated in the context of the present disclosure thatother circuits may also be used, whereby the layers of the pixel areconnected in a different manner such that a different circuit is made.The fundamental layers and the method of deposition methods would remainsubstantially consistent with the above disclosed embodiments.Advantages achieved by using the surface to be touched in a touch sensoralso as the substrate for deposition of the pixel stack may of course beprovided in other pixel circuits.

It will be appreciated from the discussion above that the examples shownin the figures are merely exemplary, and include features which may begeneralised, removed or replaced as described herein and as set out inthe claims. With reference to the drawings in general, it will beappreciated that schematic functional block diagrams are used toindicate functionality of systems and apparatus described herein. Inaddition the processing functionality may also be provided by deviceswhich are supported by an electronic device. It will be appreciatedhowever that the functionality need not be divided in this way, andshould not be taken to imply any particular structure of hardware otherthan that described and claimed below. The function of one or more ofthe elements shown in the drawings may be further subdivided, and/ordistributed throughout apparatus of the disclosure. In some examples thefunction of one or more elements shown in the drawings may be integratedinto a single functional unit.

As will be appreciated by the skilled reader in the context of thepresent disclosure, each of the examples described herein may beimplemented in a variety of different ways. Any feature of any aspectsof the disclosure may be combined with any of the other aspects of thedisclosure. For example method aspects may be combined with apparatusaspects, and features described with reference to the operation ofparticular elements of apparatus may be provided in methods which do notuse those particular types of apparatus. In addition, each of thefeatures of each of the examples is intended to be separable from thefeatures which it is described in combination with, unless it isexpressly stated that some other feature is essential to its operation.Each of these separable features may of course be combined with any ofthe other features of the examples in which it is described, or with anyof the other features or combination of features of any of the otherexamples described herein. Furthermore, equivalents and modificationsnot described above may also be employed without departing from theinvention.

Certain features of the methods described herein may be implemented inhardware, and one or more functions of the apparatus may be implementedin method steps. It will also be appreciated in the context of thepresent disclosure that the methods described herein need not beperformed in the order in which they are described, nor necessarily inthe order in which they are depicted in the drawings. Accordingly,aspects of the disclosure which are described with reference to productsor apparatus are also intended to be implemented as methods and viceversa. The methods described herein may be implemented in computerprograms, or in hardware or in any combination thereof. Computerprograms include software, middleware, firmware, and any combinationthereof. Such programs may be provided as signals or network messagesand may be recorded on computer readable media such as tangible computerreadable media which may store the computer programs in non-transitoryform. Hardware includes computers, handheld devices, programmableprocessors, general purpose processors, application specific integratedcircuits (ASICs), field programmable gate arrays (FPGAs), and arrays oflogic gates.

Other examples and variations of the disclosure will be apparent to theskilled addressee in the context of the present disclosure.

The invention claimed is:
 1. A method of authenticating a user using anelectronic device comprising a fingerprint sensor, the methodcomprising: an enrolment process comprising: obtaining first fingerprintdata of a first hand using the fingerprint sensor; determining, andstoring, features of first fingerprints of respective fingers of thefirst hand; and determining, and storing, for each one of the possiblepairs of first fingerprints, an indication of plausible displacementbetween the two first fingerprints on the fingerprint sensor; acomparison process comprising: obtaining second fingerprint data of asubsequent hand using the fingerprint sensor; determining features ofsecond fingerprints of respective fingers of the subsequent hand;determining a degree of similarity between each one of the firstfingerprints and each one of the second fingerprints based on acomparison of the respective features; identifying pairs offingerprints, each pair comprising one of the first fingerprints and thesecond fingerprint which has the highest degree of similarity to saidone of the first fingerprints; and determining a plausibility of thedisplacement of each pair relative to each of the other pairs based onthe stored indications of plausible displacement between the possiblepairs of first fingerprints; and controlling authentication of the userbased on the determined plausibility of the displacement of each pair offingerprints.
 2. The method of claim 1, wherein, in the event that it isdetermined that a likelihood that the subsequent hand corresponds to thefirst hand is above a third threshold value, updating at least one of:(i) the stored features of the first fingerprints, and (ii) the storedindications of plausible displacement between first fingerprints,wherein said update is based on the second fingerprint data.
 3. Themethod of claim 1, wherein the degree of similarity between the firstand second fingerprints is determined based on both: (i) a confidencescore of the match of respective features of the first and secondfingerprints, and (ii) a rototranslation between the two fingerprints.4. The method of claim 3, wherein controlling authentication of the useris based on a final global score, which is determined based on both: (i)the determined degree of similarity between the first fingerprints andthe second fingerprints, and (ii) the determined plausibility of thedisplacement of each pair of fingerprints.
 5. The method of claim 4,wherein in the event that the final global score is greater than asecond threshold value, it is determined that the first and secondfingerprint data belong to the same user.
 6. The method of claim 1,wherein pairs of fingerprints are only identified for pairs where it isdetermined that the respective first and second fingerprints have adegree of similarity above a first threshold value.
 7. The method ofclaim 1, wherein determining the plausibility of the displacement ofeach pair relative to each of the other pairs is based on: both (i) areference point for the first fingerprint for each of the two pairs and(ii) a rototranslation between the first fingerprint and its subsequentfingerprint for each of the two pairs.
 8. The method of claim 1,wherein, in the event that it is determined that a likelihood that thesubsequent hand corresponds to the first hand is above a third thresholdvalue, updating at least one of: (i) the stored features of the firstfingerprints, and (ii) the stored indications of plausible displacementbetween first fingerprints, wherein said update is based on the secondfingerprint data; wherein the degree of similarity between the first andsecond fingerprints is determined based on both: (i) a confidence scoreof the match of respective features of the first and secondfingerprints, and (ii) a rototranslation between the two fingerprints;wherein controlling authentication of the user is based on a finalglobal score, which is determined based on both: (i) the determineddegree of similarity between the first fingerprints and the secondfingerprints, and (ii) the determined plausibility of the displacementof each pair of fingerprints; and wherein the determined likelihood thatthe subsequent hand corresponds to the first hand comprises the finalglobal score, and in the event that the final global score is above thethird threshold value, the method comprises updating the at least oneof: (i) the stored features of the first fingerprints, and (ii) thestored indications of plausible displacement between first fingerprints,wherein said update is based on the second fingerprint data.
 9. Anelectronic device comprising a fingerprint sensor, which is capable ofsimultaneously acquiring a plurality of fingerprints of a hand, memorymeans and processing means, configured to implement the method ofclaim
 1. 10. A non-transitory computer program product, which isloadable in the memory means of an electronic device comprisingprocessing means and is designed to implement, when executed by saidprocessing means, the non-transitory computer program product configuredto implement the method according to claim
 1. 11. An enrolment method ofpreparing an electronic device comprising a fingerprint sensor toauthenticate a user using the fingerprint sensor, the method comprising:obtaining first fingerprint data of a first hand using the fingerprintsensor; determining, and storing, features of first fingerprints ofrespective fingers of the first hand; and determining, for each one ofthe possible pairs of first fingerprints, an indication of plausibledisplacement between the two first fingerprints on the fingerprintsensor; and storing the indication of plausible displacement for each ofthe possible pairs to enable the electronic device to controlauthentication of a user based on both: (i) the stored indications ofplausible displacement between two first fingerprints, and (ii) secondfingerprint data of a subsequent hand using the fingerprint sensor. 12.A comparison method of authenticating a user using an electronic devicecomprising a fingerprint sensor, the method comprising: obtaining secondfingerprint data of a subsequent hand using the fingerprint sensor;determining features of second fingerprints of respective fingers of thesubsequent hand; determining a degree of similarity between each one ofa plurality of stored first fingerprints from a first hand and each oneof the second fingerprints based on a comparison of respective featuresof the first and second fingerprints; identifying pairs of fingerprints,each pair comprising one of the stored first fingerprints and the secondfingerprint which has the highest degree of similarity to said one ofthe stored first fingerprints; determining a plausibility of thedisplacement of each pair relative to each of the other pairs based on astored indication of plausible displacement between possible pairs ofthe first fingerprints; and controlling authentication of the user basedon the determined plausibility of the displacement of each pair offingerprints.